1. Field of the Invention
This invention is generally related to monitoring systems, where data from an asset is collected and processed by a computing system.
2. Description of the Related Art
The monitoring and reporting of the anomalies could serve one or more purposes including quality assurance for manufacturing processes, improvement of operations safety (early warning of incipient problem), condition-based maintenance (CBM) of the assets, and performance monitoring. Examples of the performance monitoring are monitoring fuel consumption of a power generation system or of an aircraft with the purpose of adjusting the hardware or operational regime in case of anomaly.
The word “asset” as used herein may include a machine, an industrial plant, a vehicle, a manufacturing process, a building, a facility, a utility system, a computer network, or other engineered system. “Monitoring” here is defined as determining whether an asset is operating normally and, if not, determining more detailed information about the anomaly experienced by the asset. “Monitoring system” here includes asset data management, monitoring methods, computational logic implementing the monitoring methods, software services supplementing the computational logic, systems architecture, and an arrangement for reporting the monitoring results.
The simplest form of monitoring, known as Statistical Process Control (SPC), has been extensively used for several decades. SPC has been introduced for quality assurance when the monitored asset is a manufacturing process. The original SPC methods are univariate: a time series for a selected measured or computed parameter is compared against control limits; the exceedances of the control limits are reported as anomalies.
An extension of the SPC is Multivariate Statistical Process Control (MSPC). The MSPC monitors many data channels simultaneously and can provides significant improvement over univariate SPC monitoring of individual channels if the monitored channels are strongly correlated, as often is the case in practice. In general, MSPC requires computer processing of streaming multivariable data. The MSPC found broader use in the last two decades with proliferation of digital computers, especially for monitoring of industrial plants and processes.